Traditional fraud modeling relies on collecting large amounts of labeled historical data to build and test, with human supervision, statistical models. The fundamental assumption is that the fraudulent and normal patterns of the past will be largely consistent in the future. This supervised modeling approach has been extremely successful in building fraud detection models for which the historical data was of good quality and generalized well to future customer behavior.
However, there are many scenarios in which this traditional modeling approach is not feasible or poorly suited. Often a situation arises in which historical data from clients is not available, or the contributed historical data is of low quality: In these cases, a more appropriate choice than a traditional model is an unsupervised model which utilizes self-calibrating analytics to track the production environment.
Another situation is a changing fraud environment which causes model performance to degrade faster than expected between model retrains. A fraud feedback loop providing fraud detection information from the client directly into the model allows the model to update weights and adapt to a changing fraud environment.